Oil must increasingly be extracted from more complex geologies, thereby driving the demand for better sensor acquisition technologies, higher resolution hydrocarbon models, more iterations of analysis cycles, and increased integration of a broader variety of data types. As a result, the size of seismic datasets continues to increase. In addition, four-dimensional modeling techniques have been developed to monitor and simulate reservoirs over time, based on the acquisition of seismic data from a same area at different points in time. Thus, seismic data compression has become crucial in geophysical applications, for efficient processing, storage and transmission. A need therefore exists for improved techniques for compressing both raw and processed seismic data.
Seismic data is a special kind of time series of signal data. Signal data compression techniques typically work in two phases. In a first phase, data is represented by a function that estimates values and residuals obtained from the difference between the original and the estimated values. In a second phase, the residuals and the function parameters are entropy encoded. The final result heavily depends on how well a function can be found that accurately estimates the values so that the residuals are small. The main challenge for seismic data is the fact that it is not an easy task to find such a function due to the nature of the data.